Quantifying Nitrous Oxide Emissions from Agricultural Soils and

Chapter 1

Quantifying Nitrous Oxide Emissions from Agricultural Soils and Management Impacts Downloaded by UNIV OF SUSSEX on May 3, 2016 | http://pubs.acs.org Publication Date (Web): October 11, 2011 | doi: 10.1021/bk-2011-1072.ch001

S. J. Del Grosso*,1,2 and W. J. Parton2 1USDA,

Agricultural Research Service, 2150 Centre Ave, Bldg. D, Ste. 100, Fort Collins, CO 80526 2Natural Resource Ecology Laboratory, Colorado State University, 1231 East Drive, Fort Collins, CO 80526 *E-mail: [email protected]

Nitrous oxide (N2O) is the primary greenhouse gas associated with most non-flooded cropping systems. N2O emissions have been measured from numerous experimental plots around the world; most often using ground based chambers but recently estimates based on top down approaches have become available. Data resulting from these measurements led to the development of N2O emission models of varying complexity. Comparing N2O fluxes estimated by different methods shows that as scale increases, estimates based on different modeling and measuring approaches tend to converge. As scale decreases, complex models that simulate the plant-soil system usually agree more closely with measurements than simple models that are based on regression equations. Because about 25-50% of the N fertilizer added to soils is typically lost from the plant-soil system, there is potential to reduce N2O emissions with improved management. Promising technologies include N fertilizers with urease and nitrification inhibitors and time released fertilizers. At the farm level, complex models appear to be the best method to quantify the management impacts on emissions because extensive measuring is too expensive and simple models are not reliable at this scale. But the ability of the models to represent how available land management options interact with environmental conditions to control soil greenhouse gas emissions is incomplete and further model development and testing are required. In particular, model outputs need to © 2011 American Chemical Society Guo et al.; Understanding Greenhouse Gas Emissions from Agricultural Management ACS Symposium Series; American Chemical Society: Washington, DC, 2011.

be compared with observations of N2O emissions and other nitrogen and carbon fluxes at various spatial and temporal scales.

Downloaded by UNIV OF SUSSEX on May 3, 2016 | http://pubs.acs.org Publication Date (Web): October 11, 2011 | doi: 10.1021/bk-2011-1072.ch001

Introduction Nitrous oxide (N2O) is an important greenhouse gas with a Global Warming Potential (GWP) approximately 300 times that of carbon dioxide (CO2). With the decline of chlorofluorocarbon emissions resulting from the Montreal Protocol, N2O is now thought to be the dominant stratospheric ozone depleting substance (1). Agriculture is responsible for the majority of anthropogenic N2O emissions in the US (2) and globally (3). Burning of crop residues and manure management systems contribute to N2O emissions but the biggest source is cropped and grazed soils. The microbial processes of nitrification and denitrification are responsible for soil N2O emissions. Nitrification is the oxidation of ammonium to nitrate while denitrification is the reduction of nitrate to N2O, as well as N2 (These and other soil processes are described in detail in Chapter 17 of this volume). Both nitrification and denitrification occur naturally in soils but common agricultural practices tend to enhance their rates and cause emissions from managed soils to exceed background rates. In particular, nitrogen (N) inputs from fertilizer and manure amendments and cropping of N fixing legumes influence soil N cycling and provide substrates for nitrification and denitrification. Although the biochemistry of these processes has been studied for decades, there remains a fair amount of uncertainty in estimates of N2O emissions from agro-ecosystems. In this chapter we discuss the different methods to quantify N2O emissions and their uncertainties, and technologies to reduce emissions and increase N use efficiency.

Methods to Quantify N2O Emissions Methods to quantify N2O emissions can be placed into two broad categories, those based on measurements and those based on models. Measuring methods are further partitioned into bottom up and top down methods. Bottom up methods involve placing air tight chambers on ‘anchors’ driven into the soil (Figure 1). Changes in gas concentration measured immediately upon chamber placement and at successive time intervals (e.g., 0, 15, and 30 minutes after chamber placement) are used to infer instantaneous gas flux. Chamber methodology was developed decades ago and is responsible for the majority of soil N2O flux observations. Chambers provide snapshots of emissions at fine temporal and spatial resolution and are appropriate for plot level studies. However, disturbance of vegetation and soil are required and because spatial and temporal variability of N2O fluxes are high, sufficient sampling frequency and spatial coverage of anchors are required. Studies comparing measurements from automated chambers (several measuring periods per day) with manual measurements of different frequencies suggest that measuring frequency should be at least once per week to ensure reasonable agreement with results from more intensive sampling (4, 5). 4 Guo et al.; Understanding Greenhouse Gas Emissions from Agricultural Management ACS Symposium Series; American Chemical Society: Washington, DC, 2011.


Figure 1. Ground level chamber method for measuring soil surface trace gas flux.

Top down methods do not appreciably disturb the system and have a larger spatial footprint than chambers. The eddy covariance method originally developed to measure land surface CO2 and H2O vapor fluxes have recently been adapted for N2O (Figure 2). Continuous monitoring of N2O concentration and vertical wind speed at a given height (e.g., 3 meters) above the surface are used to calculate gas flux at small time intervals (e.g., 15 minutes). The spatial foot print is a function of instrument height and horizontal wind speed. This method is appropriate for the field scale and provides almost continuous sampling through time. However, it cannot distinguish emissions from plots receiving different treatments and is impacted by wind velocity. In particular, low night time vertical mixing precludes accurate measurements of night time fluxes. Tower-based systems have similar advantages and disadvantages of eddy covariance methods but use the flux gradient technique (6). N2O measured from instruments placed at different heights (e.g., 3 m and 2 m) and wind velocity measurements are used to calculate flux rates. Aircraft-mounted gas concentration and wind velocity sensors have also recently been used to monitor N2O fluxes over even larger scales using the eddy covariance method (7). Disadvantages of this method include expense and limited temporal coverage, since measurements can be made only for discrete time periods. The global top down method estimates N2O emissions based on measurements of atmospheric concentration of N2O through time and estimates of the photochemical sink strength in the stratosphere (8). This method integrates over the entire globe and thus cannot be used for greenhouse gas source attribution, but does provide a constraint for global estimates scaled up by using other methods.

5 Guo et al.; Understanding Greenhouse Gas Emissions from Agricultural Management ACS Symposium Series; American Chemical Society: Washington, DC, 2011.


Figure 2. Flux tower for measuring trace gas flux from the plant-soil system.

Models used to quantify soil surface N2O emissions range from simple empirical equations to complex models that simulate the processes that control emissions. The most commonly used empirical method is the Tier 1 Intergovernmental Panel on Climate Change (IPCC) emission factor approach which assumes that 1% of N applied to soil from different sources (e.g., fertilizer amendments, crop residues) is emitted as N2O-N on an annual basis (9). This method is simple to apply and calculations are highly transparent but the uncertainty range is large (-70% to +200%). Models that simulate important processes in the plant-soil system (e.g., soil water and heat fluxes, net primary productivity, biomass senescence and harvest, organic matter decomposition, nutrient mineralization, nitrification, denitrification) usually have smaller uncertainty ranges but results are not highly transparent and more complex input data as well as sufficient computing resources are required.



DayCent (Daily Century) is a process based model of intermediate complexity widely used to estimate soil N2O emissions and other flows of carbon and nutrients. DayCent is the daily time step version of the CENTURY model (10). CENTURY was developed in the 1970’s to simulate changes in plant growth, soil organic matter cycling, and other ecosystem factors in response to changes in land management and climate (11). CENTURY operates at a monthly time step, which is adequate to model plant growth and soil organic matter changes. However, finer resolution is required to simulate soil trace gas (N2O, NOx, CH4) fluxes. DayCent uses readily available inputs and has the ability to simulate common disturbance and management events (e.g., fire, grazing, cultivation, harvest, irrigation, fertilization). DayCent simulates exchanges of carbon, nutrients, and trace gases among the atmosphere, soil, and plants (Figure 3). Required model inputs are: soil texture, current and historical land use, and daily maximum/minimum temperature and precipitation data. Plant growth is a function of soil nutrient and water availability, temperature, and plant specific parameters such as maximum growth rate, minimum and maximum biomass carbon to nutrient ratios, and above vs. below ground carbon allocation. Soil carbon levels fluctuate according to inputs from senesced biomass (after accounting for biomass removal during harvest operations and disturbance events) and manure amendments, as well as losses from microbial respiration. Nitrogen gas (N2O, NOx, N2) emissions from nitrification and denitrification are controlled by soil mineral N (nitrate and ammonium) levels, water content, temperature, pH, plant N demand, and labile C availability. Nitrate leaching losses are controlled by soil NO3 availability, saturated hydraulic conductivity, and water inputs from rainfall, snowmelt, and irrigation. The ability of DayCent to simulate crop yields, soil organic matter, N2O emissions, and nitrate (NO3) leaching has been validated by comparing model outputs with measurements from various cropped and grassland systems in North America (12–14). DayCent has been applied to simulate soil greenhouse gas fluxes at scales ranging from plots to regions to the globe (10, 12, 15). The model has been used since 2005 to calculate N2O emissions from agricultural soils for the U.S. National Greenhouse Gas Inventory complied by the Environmental Protection Agency (EPA) and reported annually to the United Nations Framework Convention on Climate Change (2, 16). Some model limitations include: not accounting for the influence of topography on lateral water and nutrient flows, ammonium (NH4) is considered to be immobile and is confined to the top 15 cm soil layer, and the impacts of microbial community diversity on biochemical processes are discounted. With recent technological advances (i.e. increased access to databases with needed model driver data and development of user friendly interfaces) it is becoming easier for non-experts to use complex simulation models to estimate emissions (e.g., (17)). The COMET-VR_2.0 – Decision Support System for Agricultural Greenhouse Gas Accounting is designed to be used by land managers and is freely available (http://www.comet2.colostate.edu/). The package is easy to use and conducts DayCent simulations to quantify greenhouse gas emissions as well as uncertainty intervals. 7 Guo et al.; Understanding Greenhouse Gas Emissions from Agricultural Management ACS Symposium Series; American Chemical Society: Washington, DC, 2011.


Figure 3. Flow diagram for the DayCent biogeochemical model.

Uncertainties and Comparisons of Methods There are pros, cons, and uncertainties associated with all available methods to quantify soil N2O emissions. Measurements represent our best estimate of ‘truth’ but it is prohibitively expensive to achieve complete spatial and temporal coverage and measuring methods often disturb the system. Models do not alter 8 Guo et al.; Understanding Greenhouse Gas Emissions from Agricultural Management ACS Symposium Series; American Chemical Society: Washington, DC, 2011.


the system and can provide more complete spatial and temporal coverage, but are simplifications of reality. It is thus important to compare estimates using the different methods to better inform these strengths and weaknesses and to improve uncertainty assessments. It has long been recognized that N2O emissions are highly variable in space and time, uncertainty in both measured and modeled estimates can be quite high, and coefficients of variation (CV) for measured N2O can exceed 100% (e.g. (18)). But recent improvements in methodologies and instrumentation have narrowed the uncertainties and CV for daily measurements are often less than 50% (e.g. (19–21)). Improvements in uncertainties have also been recently achieved for model generated estimates of emissions. Del Grosso et al. (10) developed a methodology to rigorously account for both uncertainty in model inputs and model structure. Probability distribution functions were developed for model inputs and a series of Monte Carlo simulations were conducted to assess uncertainty due to lack of precise input data. But even if inputs were precisely known, there is still error due to model structure because the algorithms and parameters in the model are imperfect representations of reality. A statistical equation was derived by comparing model outputs with measured emissions from experiments in North America to quantify model structural uncertainty. The calculated uncertainty for N2O emissions for cropped soils in the US using this method (-33% to +50%) is smaller than previous estimates ranging from -70% to +184% (22) to +/- 57% (23). Uncertainties in soil N2O emissions can also be addressed by comparing estimates derived from different methods. Nitrous oxide measured from forests in Finland and Denmark using eddy covariance was found to agree well with ground based chamber measurements (24). Flux tower and aircraft based measurements of N2O from an agricultural area in eastern Canada showed good agreement after accounting for differences in footprint sizes and landscape make up. Nitrous oxide emissions from agriculture calculated using the Tier 1 IPCC (2006) methodology agreed surprisingly well with emissions calculated using a top-down approach (8) at the global scale (25). Both methods estimated that about 4% of newly fixed N from fertilizer production and legume cultivation is emitted as N2O-N from agricultural production systems. Although the default IPCC emission factor is only 1% for N from fertilizer and managed manure added to soils, this factor also applies to N in unharvested crop residue; also, the emission factor is 2% for N in unmanaged manure deposited onto soil by grazing livestock. Additionally, the IPCC method includes N2O from the following sources: indirect N2O resulting from N that left the farm in a form other than N2O (e.g., volatilized NH3, leached NO3) and was converted to N2O offsite, and N2O from manure management systems. When all of these sources of N2O are considered from a life cycle perspective, approximately 4% of newly fixed N from fertilizer production and legume fixation is assumed to be converted to N2O and released into the atmosphere. A general pattern is that as scale increases, different methods of calculating emissions tend to converge (25). Similarly, model outputs for N2O emissions often show poor agreement with observations at the daily scale, but good agreement when emissions are aggregated to seasonal or annual values (7) and model uncertainty tends to decrease as scale increases (10). 9 Guo et al.; Understanding Greenhouse Gas Emissions from Agricultural Management ACS Symposium Series; American Chemical Society: Washington, DC, 2011.


Nitrous Oxide Mitigation Approximately 50-75% of N inputs to crops from fertilizer amendments and fixation are typically removed during harvest (26) thus, there is an opportunity to increase N use efficiency while decreasing N2O emissions and other N loss pathways. The primary reason for these losses is that N availability is not entirely synchronous with plant N demand so at least during some time periods, N is in excess. This excess N is eligible to be leached below the rooting zone during sufficiently intense rainfall events and can be converted to gaseous compounds by soil biochemical processes and lost from the plant-soil system. In contrast, native systems that are not fertilized are more N limited and rarely experience conditions when N availability greatly exceeds plant N demand. This is because N is gradually released in small amounts during decomposition of soil organic matter and dead plant material. In sum, the N cycle is much tighter in native systems and usually less than 10% of the N that cycles is lost on an annual basis. Thus, the challenge is to make managed systems more like native systems in that excess N is minimized, while at the same time ensuring that available N is sufficient to satisfy plant demand. Strategies to reduce N losses include application of nitrification and urease inhibitors, time released fertilizer, timing fertilizer application events to be more synchronous with plant N demand, strategically placing fertilizer in the rooting zone instead of uniformly across the soil surface, and reducing the amount of N fertilizer applied. When evaluating these mitigation options, results should be presented per unit of product. For example, greatly reducing N fertilizer additions would reduce emissions substantially on a unit area basis but yields would likely decrease as well, thus necessitating an increase in the amount of cropped land to keep total yield constant. It should also be noted that since the early 1980’s, the amount of fertilizer applied to US crops has increased by only ~20%, but crop yields have almost doubled (27). To help continue these N use efficiency gains the ‘4 rights’ regarding fertilizer application are advocated: apply the right product, at the right rate, during the right time, and at the right place (28). The right product is often ammonium instead of nitrate based, and includes nitrification and urease inhibitors, or polycoated urea. The right rate is based on soil N availability and yield goals. The right time is usually during the beginning of the growing season and the right place is often below the soil surface or banded on the surface so that more fertilizer intersects plant roots. Practicing the ‘4 rights’ outlined above minimizes N losses during the growing season but non growing season losses can still be substantial. If unexpected weather events result in lower yields than anticipated, then at least a portion of the excess N that was not taken up by plants and harvested is likely to be lost during the non-growing season. Even when yields meet or exceed expectations, decomposition of unharvested plant residues and soil organic matter mineralizes N that can then be lost via leaching or gaseous emissions. An effective way to minimize these N losses is to grow winter season cover crops to scavenge excess N (29). However, cover crops are often not profitable for farmers so incentives are usually needed to encourage cover crop adoption. In addition to N losses, we also advocate accounting for the impacts of different management alternatives on 10 Guo et al.; Understanding Greenhouse Gas Emissions from Agricultural Management ACS Symposium Series; American Chemical Society: Washington, DC, 2011.


other greenhouse gas fluxes (CO2, CH4). The economic costs/benefits of different alternatives, as well as the potential incentives, also need to be considered. Programs to incentivize improved N management require methods to assess the reduction in emissions achieved under different management options. However, it is not feasible to sample individual farms at the required intensity to accurately determine N2O emissions. Default Tier 1 IPCC methodology (9) provides reasonable estimates of N2O emissions at large scales and can be used to estimate emissions at the farm level, but evidence shows that this methodology is often not reliable at the experimental plot level (25). This suggests that default Tier 1 methodology is also not reliable at the farm level. Complex models account for site level impacts not included in the default Tier 1 methodology and have been shown to be reasonably reliable at the plot scale, and are expected to be more reliable than default Tier 1 methodology at the farm level. Recent improvements in computing hardware and software development now facilitate the use of farm level decision support tools such as the COMET tool discussed above. However, current tools are limited in that they do not adequately represent all of the currently available mitigation technologies and have been tested for only a limited number of cropping systems. For example, the DayCent model, which is used to calculate N2O emissions in the COMET tool, does not realistically simulate the impacts of fertilizer placement, and has only been extensively tested for major commodity crops. To increase confidence in tools such as COMET, model outputs need to be compared with observations of N2O emissions and other nitrogen and carbon fluxes at various spatial and temporal scales. Fortunately, data sets that include measurements of N and C fluxes, as well as model driver data, are becoming increasingly available.

Conclusion Nitrous oxide emissions are difficult to quantify using measurements because variability in space and time is high. It is also difficult to model emissions because various factors interact, often non-linearly, to control emissions. But recent advances in measuring and modeling technologies have significantly improved emission estimates and future estimates should be even more reliable. Better quantification of the temporal and spatial variability in emissions and improvement of mitigation technologies allow incentive programs to identify region and farm level specific best management practices that limit the release of N2O and other greenhouse gases while maintaining high crop and forage yields.

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Quantifying Nitrous Oxide Emissions from Agricultural Soils and

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